STEM Program

Navigating An Interconnected World: Algorithmic, Computational, and Statistical Methods and Their Applications

Faculty Advisor: Professor, the School of Computer Science, Georgia Institute of Technology

Research Program Introduction

Complex systems, both living and man-made, can be represented as static or dynamic networks of many interacting components. The Science of Networks is a necessary and relatively new discipline in STEM and Computer Science that investigates the topology and dynamics of complex networks, aiming to understand better the behavior, function, and properties of the underlying systems. The applications of network science cover physical, informational, biological, cognitive, and social systems. 

In this research program, students will study algorithmic, computational, and statistical methods of network science and applications in communications, biology, ecology, brain science, sociology, and economics. The course will go beyond the strictly structural concepts of small-world and scale-free networks, focusing on dynamic network processes such as epidemics, synchronization, or adaptive network formation.

This program introduces students to key concepts, algorithms, and metrics in studying networks. Students will also get hands-on experience in network analysis using Network-X. Through the program, students will gain valuable first-hand experience with computational research, focusing on network topics of practical significance and broad social interest. 

Students will focus on individual topics and generate their work products upon completing the program - short research reports summarizing their projects, what they have done, and what they have discovered. Students will also learn general and subject-specific research and academic writing methods used in universities and scholarly publications.

Possible Topics For Final Project:

  • Social networks: Detection of misinformation in social networks

  • Epidemic networks: The structure of epidemic networks for different pathogens

  • Deep learning: Hierarchical networks and network models in deep learning

  • Transportation: The multi-layer networks of transportation and human mobility

  • Network models: Cooperation and conflict

  • Network models: Innovation and novelty adoption

Program Details

  • Cohort size: 3 to 5 students

  • Workload: Around 4 to 5 hours per week (including class and homework time)

  • Target students: 9 to 12th graders interested in STEM studies, such as Computer Science, Machine Learning, AI, Math, Data Science, Engineering, etc. Students are NOT expected to have formal training in calculus, probability, or linear algebra. Some basic knowledge of Python would be helpful but not mandatory. 

  • Schedule: TBD. Meetings will take place for around one hour per week, with a weekly meeting day and time to be determined one week before the class start date.